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Coupled MCMC (MC3) for BEAST

Parallel tempering (Metropolis-coupled MCMC) for BEAST 3.

Maven coordinates

<dependency>
    <groupId>io.github.compevol</groupId>
    <artifactId>coupled-mcmc</artifactId>
    <version>1.3.0-beta1</version>
</dependency>

JPMS module: coupled.mcmc

How to set up your analysis to run with coupled MCMC

Using the conversion app

After installing the CoupledMCMC package, the MCMC2CoupledMCMC app becomes available in the app launcher.

  1. Create an MCMC analysis in BEAUti with any of the available templates, save as mcmc.xml

  2. From a terminal, run:

    /path/to/beast/bin/applauncher MCMC2CoupledMCMC -xml mcmc.xml -o mc3.xml

    This creates a file mc3.xml containing a CoupledMCMC analysis with the same model/operators/loggers as the mcmc.xml analysis.

    Alternatively, from BEAUti use menu File > Launch apps, select MCMC to Coupled MCMC converter from the available apps, fill in the form and click OK.

By editing an XML file

In a pre-prepared XML, replace the MCMC run element:

<run id="mcmc" spec="MCMC" chainLength="....." numInitializationAttempts="....">

with:

<run id="mcmc" spec="coupledMCMC.CoupledMCMC" chainLength="100000000" storeEvery="1000000" deltaTemperature="0.025" chains="2" resampleEvery="10000">
  • deltaTemperature="0.025" — temperature difference between chain n and chain n-1. Tune so that the swap acceptance probability is between 0.25 and 0.6.
  • chains="2" — number of parallel chains. The first (cold) chain explores the posterior; heated chains have higher acceptance probabilities and propose new states to the cold chain.

Citation

Müller NF, Bouckaert RR. Adaptive Metropolis-coupled MCMC for BEAST 2. PeerJ. 2020 Sep 16;8:e9473. DOI:10.7717/peerj.9473

Altekar G, Dwarkadas S, Huelsenbeck JP, Ronquist F. Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics 20(3):407–415